Thursday, September 6, 2012

This is an update to a
post from last year. The post itself has not been updated.
You can find the complete article on agent.ch.

Experts met on January 19, 2012 at the International Telecommunication Union
to decide whether to abolish leap seconds. Due to a lack of consensus among
participants it was decided to postpone the decision
(BBC News).
It was a classical standoff between those who want sharp, systematic
solutions and the advocates of
if
it ain't broke, don't fix it. A few months later, on June 30, 2012,
yet another leap second was added, causing problems at some websites.
Wired reported it under the dramatic sounding heading
The
Inside Story of the Extra Second That Crashed the Web.
The discussion is open: is it broke? Can we fix it?

Wednesday, August 29, 2012

Yesterday, I moved some of my Java projects to Maven.
There is no difference from a client point of view. The new JARs are plug-compatible
but not identical, so checksums have changed. This is the only reason why minor version numbers have been +=1d.

The software is not yet available from The Central Repository,
but can be found in a freely accessible maven repository on GitHub.
To use it, specify this in your POM or settings:

Tuesday, July 17, 2012

There were quite a few methods named delete in the CrNiCKL software.
This was a bad idea because such methods are useless when called from JavaScript.
Indeed, delete is a reserved word in js.

All such methods have been renamed and
a new version of the software has been released to the
project website,
to SourceForge and the GitHub repositories. The following scheme
has been used for renaming. Parameterless delete methods are now
named destroy. The others are now typically named deleteFoo
where Foo is the type of (one of) the parameter(s).

I decided to completely eliminate the old methods rather than let them live a bit longer as deprecated. I did it because the software is not yet used out there. In case I misread the stats, please accept my apologies. In any case the previous version remains available on all the websites mentioned.

Friday, July 13, 2012

Since a very long time I wanted to switch to the git version control system. It's now done. Compared to what I've used in the past: cvs, sccs, rcs, vss, sclm, Librarian, and maybe others I have forgotten, I like git better. I don't know why I waited so many years...

Although the source of the projects I discuss in this blog was already available for downloading via the project websites,
it can now be browsed more comfortably at GitHub.

The ChronoDB project has been renamed CrNiCKL. I noticed a naming conflict with an unrelated product and decided to change the name when it's still harmless.

Chronicles are the most important objects in the ChronoDB CrNiCKL database. So I started from them to find a new name. I settled on CrNiCKL because it can be pronounced like "chronicle" and there are practically no hits for it in Google. I also decided to write the name with most of the letters in uppercase, so it stands out in text.

The software remains available under its old name, for a while at least, but won't be updated. Blog posts related to ChronoDB will not be updated but will get a notice about the rename.

Wednesday, July 11, 2012

The marketing departement has just updated the
home page of the Time2 Library project with
the following quote of the day:

If you need speed, compactness, and flexibility, then the Time2 Library is
for you. There is no trade off. Even with the wildest time domains
(calendars if you prefer) a time point is just a number.
In many cases it is even less than that: it is an array index.
The flexibility comes into play only if and when needed,
for example when printing or scanning a date.

Monday, July 9, 2012

Important notice. On July 13, 2012, the ChronoDB project was renamed CrNiCKL, which is pronounced like "chronicle". All packages, demos included, have been renamed. The new project website is at http://agent.ch/timeseries/crnickl/. The old project remains accessible for a while
at http://agent.ch/timeseries/chronodb/. The remainder of this article remains valid mutatis mutandis.

I've just released another ChronoDB demo to show how to set up a
database with time series of geographical coordinates.
The demo can be found in the package ch.agent.chronodb.demo.geocoord
in archive chronodb-demo-1.1.0.jar
at the ChronoDB project website or on SourceForge.

GeoCoord is a toy Java interface for geographical coordinates, with three methods:
boolean isNear(GeoCoord coord);
boolean isNear(GeoCoord coord, double distance);
double distanceTo(GeoCoord coord);
In the demo, GeoCoords are implemented as
cartesian coordinates. The glue with ChronoDB is provided by a simple value scanner GeoCodeValueScanner and a less simple implementation of
ValueAccessMethods<GeoCoord> named
AccessMethodsForGeoCoord. The Database class itself is so
small it can be listed completely here:
package ch.agent.chronodb.demo.geocoord;
import ch.agent.chronodb.jdbc.JDBCDatabase;
public class GeoCoordDatabase extends JDBCDatabase {
public GeoCoordDatabase(String name) {
super(name);
setAccessMethods(GeoCoordValueScanner.class.getName(),
new AccessMethodsForGeoCoord());
}
}

To make things interesting the demo uses a special time domain, with
time points at 7:00 AM, 9:00 AM, 3:11 PM, and 9:33:20 PM every Monday, Tuesday,
Friday, and Saturday. For a refresher on time domains, please
have a look at my previous post.

Friday, July 6, 2012

This note is an edited summary of the ideas underlying the design of the
Time2 Library.

With time playing a rather important role in this world,
sequences of things ordered by time are present in many kinds of systems.
Because the idea of time is intuitively familiar, it is tempting to choose
a simplistic design when modeling a system or even not to think about it at all.
Ironically, simplistic designs can lead to needlessly complex implementations,
as annoying issues are addressed one after the other.

Stated informally, a time series is a set of elements uniquely identified
by a discrete point in time or by a time interval.
The Time Series Framework requires a more precise definition:

A time series is characterized by a value type and a time domain.
All elements of a time series have a value of the same type, the value type,
or can be recognized as missing. Any element can be uniquely identified
by a point in the time domain of the series.

Note that the definition does not explicitly allow for time ranges identifying
values. This is generally not a problem because of the nature of time domains.

The terms used in the definition will be explained shortly, but before
that a few typical problems will be discussed. These problems should
illustrate why a framework for time series is useful.

Problems and design goals

Let us look at a series alternating between two
constant values every working day: Friday=5, Monday=10, Tuesday=5,
Wednesday=10, Thursday=5, Friday=10, Monday=5, Tuesday=10, and so
on. The series has never any data on weekends. Consider
this straightforward chart plotting the values on the
vertical axis against their dates, on the horizontal axis:

This looks a bit funny, doesn't it? The chart does not express very well the
perfect regularity of the series. Compare it to
this designed solution:

The visual pattern is now a faithful expression of the regularity of
the data. The chart does it by excluding weekends. The time
domain is in fact the only difference between the two charts:
everyday dates in one case versus workweek-only dates in the other.

Tel Aviv, Cairo, New York

Beyond this contrived example, there are many situations with
no data on weekends. A familiar case is provided by stock markets.
These are also a good illustration of the annoying details hiding in
seemingly simple problems: weekends are not the same in Tel Aviv,
Cairo, and New York. When designing a database for global stock market
data, or when drawing charts to compare the prices of some American,
Egyptian, or Israeli stocks, such issues must be dealt with.

Getting a good grip on the time domain is the first important design
goal of the Time Series Framework.

Missing values

The following table lists the first eleven Olympic Games.

1896

Athens

1900

Paris

1904

Saint-Louis

1908

London

1912

Stockholm

1916

1920

Antwerp

1924

Paris

1928

Amsterdam

1932

Los Angeles

1936

Berlin

Eleven? It is true that there are ten Games, but it is also true that
the list has eleven elements. Is something wrong here? No. Games had
been scheduled in Berlin in 1916 but were canceled because of the
war. Conceptually, the Games of 1916 are a missing value, and
there are many real world phenomena modeled with time series where it
is common to have some values missing. A system dealing with time
series must be capable of dealing with such cases gracefully and in a
useful way. You don't want your software to return the nine first
Games when you asked for ten, or to crash on the Games of 1916. Or you
don't want your portfolio evaluation software to give up when a quote
is missing. And as a software developer, you don't want to invent ad-hoc
solutions all the time.

Detecting missing values and handling them intelligently is the
second important design goal of the Time Series Framework.

The pieces of the puzzle

Time domain

A time domain identifies a point in time with an offset from a
base time. The offset is a long integer (64 bit signed integer).

The base time is January 1 0000, corresponding to the index
value 0.

A pseudo Gregorian calendar with the usual definition of leap
years is used. It is not the true thing because it applies from year 0, and
dates before the Gregorian cutover of October 15 1582 do not correspond to
historical dates. Years can be as a large as the size of the numbers used in
the implementation allow.

The resolution of the time series defines the time unit
corresponding to an offset increment. Available units are microsecond, second,
minute, hour, day, month, year. Week is omitted by design.

The origin is a non-negative number of units defining an
offset from the base time. It plays an important role in the time patterns
introduced below. It can also be used to avoid using large integers for indexes
in applications where the range size of a series always fits within a standard
integer. This is especially useful if the programming language does not
support long integers for indexing arrays, like 64 bit Java.

The time pattern defines the time points for which elements
exist. One such pattern is a repeating cycle. A weekly series has a resolution
of day units with a cycle of one day with an element, followed by six days
without elements. A workweek series has day units with a cycle of five days
with elements, followed by two days without elements. The Olympics have year
units with a four-year cycle of one year with games, followed by three years
without. By default, a series has no cycle, meaning elements exist for all
times. The cycle starts with the origin of the time domain. It is possible
for two series with the same resolution and cycle, but not the same origin,
to have no time point in common. The summer and winter Olympics are such a
case.

Time index

A time index is in a time domain and carries a discrete offset which defines
a point in time. Two time indexes in the same time domain can be compared,
with a larger index corresponding to a later point in time. Adjacent points
in time are represented by adjacent offsets.

Because time indexes are expected to be used as keys it is important to
implement them as immutable objects.

Time range

A time range is a pair of time indexes in the same time domain, called
the lower and upper bound. If the lower bound is larger than the upper bound
the range is said to be empty.

Observation, value type, missing value

An observation has a time index and a value of some type. The value type
must allow the definition of a special value representing missing values,
without restricting the set of useful values; null (nil) can only be used
as this special value if it has no other meaning in the relevant context.

Time series

A time series maps a set of time indexes to values. All time indexes are
in the same time domain and all values are of the same type. For this
reason we talk of the time domain and the value type of a time series.

A time series can be defined alternatively as a set of observations,
with each element of the set uniquely identified by its time index.

A time series has a range defined by the smallest and largest time indexes
of the included observations.

A time series maintains the abstraction of missing values, which correspond
to time indexes in the range for which no observation exist. This definition
implies that a time series can never have missing values at its boundaries.
This is also true for a subset of a time series, because it is also a
time series.

Time series storage

Although an implementation detail, the storage of time series presents an
interesting design question. That a series maps time indexes to values does
not mean that it has to be stored in a dictionary keyed by time indexes. The
Flyweight pattern provides a better solution, making use of
the fact that for a given time domain, time indexes can be represented by
integer values, the offsets.

Two cases must be considered, depending on the frequency of missing
values. In a regular time series, missing values are
exceptional. In a sparse time series, they are the rule.

Sparse series are useful for managing irregular events. They are typically
implemented as dictionaries. Missing values are not stored.
Instead of time indexes, only offsets are used as keys. The time domain
is stored only once. Time indexes are reconstructed if and when needed.

The storage of regular series is straightforward and efficient.
All values, missing or not, are stored into an array. With missing values the
exception, the overhead is reasonable, and because they are self-signaling,
missing values are always detectable. In addition to the array, the series
stores also the time domain and the offset of the time index of the value in
the first array element. A time index can be reconstructed from the time domain
and the sum of the stored offset and the array index of the value.

Important notice. On July 13, 2012, the ChronoDB project was renamed CrNiCKL, which is pronounced like "chronicle". All packages, demos included, have been renamed. The new project website is at http://agent.ch/timeseries/crnickl/. The old project remains accessible for a while
at http://agent.ch/timeseries/chronodb/. The remainder of this article remains valid mutatis mutandis.

This post is the second of a two-part commentary on the ChronoDB demo.
In the first part I explained the steps for setting up a ChronoDB database before it can be used to perform useful work.

Setting up the schema for the demo happens on the last line of our code snippet:
StocksAndForexDemo demo = new StocksAndForexDemo(args[0]);
demo.setUpHyperSQLDatabase();
demo.setUpSchema();
Let's focus on this method.
public void setUpSchema() throws Exception {
StocksAndForexSchema schema = new StocksAndForexSchema(db);
schema.createSchema();
// commit all changes
db.commit();
}
Whenever the symbol db appears in this post, it stands for the ChronoDB database.
ch.agent.chronodb.api.Database db;
The demo needs numerical series and textual attributes. So we need to create two value types. This is done in the StocksAndForexSchema constructor:
db.createValueType("text", false, ValueType.StandardValueType.TEXT.name())
.applyUpdates();
db.createValueType("numeric", false, ValueType.StandardValueType.NUMBER.name())
.applyUpdates();
Here we use built-in support for numbers and texts provided by ChronoDB. In other cases we will provide a custom ValueScanner. But the constructor is not finished with its work. It needs to tell ChronoDB that we are going to have numeric series:
UpdatableValueType uvtvt = db.getTypeBuiltInProperty()
.getValueType().typeCheck(ValueType.class).edit();
uvtvt.addValue(uvtvt.getScanner().scan("numeric"), null);
uvtvt.applyUpdates();
The method invocation applyUpdates() seen here and there consolidates all pending modifications to an object but does not commit them to permanent storage.

When the constructor is done, things get more specific, as can be seen from the code of createSchema:
public void createSchema() throws T2DBException {
createCurrencyValueTypeAndProperty();
createSeriesUnitValueTypeAndProperty();
createTickerProperty();
createStocksSchema();
createExchangeRatesSchema();
createTopLevelChronicles();
}
In the demo we have a class for currencies and we want to use a custom value scanner:
UpdatableValueType uvt =
db.createValueType("Currency", true,
"ch.agent.chronodb.demo.CurrencyValueScanner");
The Currency object in this demo is not very useful. In a real world application it would have
more responsibilities, like computing exchange rates. Its purpose here is only to show how we
set up ChronoDB to use problem-related classes. Not shown here is how to add currency values and how to create the currency property, as it's straightforward. We also skip the details of creating other value types and properties and turn to the creation of a schema for stocks.

We want to represent a stock using a chronicle with two attributes, ticker, and currency, and with three series, price, volume, and splits. Price and volume have a custom series attribute, unit. The first thing to do is to create a schema.
The demo does not use the possibility to inherit from another schema.
UpdatableSchema schema = db.createSchema("Stocks", null);
Attributes are then added to the schema. It is necessary to provide numbers for
attributes and series. At first sight one would ask why are these numbers not hidden by the software. The answer is schema inheritance and the possibility to not only remove and modify attributes and series but also to insert them in precise positions.
schema.addAttribute(1);
schema.setAttributeProperty(1, db.getProperty("Ticker", true));
schema.addAttribute(2);
schema.setAttributeProperty(2, db.getProperty("Currency", true));
The following piece of code defines the price series as a workweek numeric series, with a currency unit:
schema.addSeries(1);
schema.setSeriesName(1, "price");
schema.setSeriesDescription(1, "close price");
schema.setSeriesType(1, db.getValueType("numeric"));
schema.setSeriesTimeDomain(1, Workday.DOMAIN);
schema.addAttribute(1, 5);
schema.setAttributeProperty(1, 5, db.getProperty("Unit", true));
schema.setAttributeDefault(1, 5, "currency");
By default a series does use sparse time series. The automatic use of sparse time series can be configured in the schema. This is done for splits series in the demo.
Even if not configured, applications still have the possibility to force sparsity when getting data.

The last step in setting up the schema is to create top level chronicles. The demo uses two collections: stocks and exchange rates. The code below shows how to create the top chronicle hosting the exchange rate collection.
Schema forexSchema = db.getSchemas("Forex").iterator().next();
UpdatableChronicle forex = db.getTopChronicle().edit()
.createChronicle("forex", false, "Exchange rate data", null, forexSchema);
forex.applyUpdates();
To create a chronicle you need to have a parent chronicle. For a top-level chronicle, the parent is the top chronicle, which is virtual and which is named after the database, "demo" in this case.

With top-level chronicles created, the demo can go ahead.
Once the required schemas have been set up, application rarely, if ever, need to do anything about them. Millions of chronicles can be created, and their attributes and series are automatically available without dong anything, except setting specific values.
In many cases it is not even necessary to set values of attributes. Take the case of american stocks. It is a simple matter to define a schema for them, inheriting from
the "Stocks" schema we made in the demo:
UpdatableSchema schema = db.createSchema("American stocks", "Stocks");
Property currency = db.getProperty("Currency", true)
.typeCheck(Currency.class);
schema.setAttributeDefault(2, currency.scan("USD"));
With this new schema, you can now create a chronicle collection for american stocks (perhaps a nested collection of the stocks collection), and all members will automatically be in USD.

As a final remark, it is important to note that as the default value of a chronicle attribute can always be overriden (it's named a default value after all), the value of a series attribute cannot. It keeps its default value, as defined in the schema. If you say in the schema that the unit of a series foo is bar then in all collections having this schema, the foo series unit will be bar. The same goes for built-in attributes: name, type, time domain, and sparsity of a series cannot be changed once defined. This does not restrict the modeling freedom. As the demo as shown you can have price series in different currencies. The price and volume have a unit ("in currency", and "in number of shares"), but the price series unit "in currency" simply tells to look at the chronicle's currency. So you will have your Toyotas in quoted in yens and your Renaults in euros.

Thursday, July 5, 2012

Important notice. On July 13, 2012, the ChronoDB project was renamed CrNiCKL, which is pronounced like "chronicle". All packages, demos included, have been renamed. The new project website is at http://agent.ch/timeseries/crnickl/. The old project remains accessible for a while
at http://agent.ch/timeseries/chronodb/. The remainder of this article remains valid mutatis mutandis.

A demo package is available for downloading from the ChronoDB project website or from SourceForge. In a short series of posts I will comment on a few important details. I hope these explanations will be helpful.

Explanations will focus on the following code snippet from the static main method of StocksAndForexDemo:
// args[0] is name of parameter file with key-value pairs ...
StocksAndForexDemo demo = new StocksAndForexDemo(args[0]);
demo.setUpHyperSQLDatabase();
demo.setUpSchema();
// etc.

The constructor invocation new StocksAndForexDemo(args[0])
initializes a ChronoDB database, kept in a private member inside the demo:
private ch.agent.chronodb.api.Database db;
The actual work is done by ch.agent.chronodb.api.SimpleDatabaseManager, which is one of the few non-interface classes in that package. It is provided to make it easier to write test cases (and demos). It sets up the database using parameters provided in a file on the file system or the class path. Here is an extract from such a file:
db.name=demo
db.class=ch.agent.chronodb.jdbc.JDBCDatabase
session.jdbcDriver=org.hsqldb.jdbc.JDBCDriver
session.jdbcUrl=jdbc:hsqldb:mem:demodb
session.db=
session.user=sa
session.password=
# etc.
Parameters with names beginning with "db." are the most important: db.name names the database and must be unique within a running system; db.class names the implementation class. Although it is in principle possible to run multiple databases simultaneously, SimpleDatabaseManager currently supports only one database. Parameters beginning with "session." are specific to the JDBC implementation. Implementations more sophisticated than JDBCDatabase used in this simple demo will have more parameters, some of which are named in the interface ch.agent.chronodb.impl.DatabaseBackend.

At this point, a ChronoDB database object and a JDBC connection are ready for use but there is absolutely nothing in the database yet. In fact, the demo uses an in-memory HyperSQL database. Before the demo starts and after the demo terminates, the database does not exist. So the next step is to create the tables and indexes expected by the JDBC implementation of ChronoDB.

This is done by the method invocation demo.setUpHyperSQLDatabase() which sends SQL data definition language (DDL) statements to the database engine for execution. The DDL is taken from Resources/HyperSQL_DDL_base.sql. This file is in
chronodb-jdbc-1.0.0.jar and therefore on the class path. The DDL defines all tables required by the base system, with various indexes and constraints to enforce referential integrity. Browse the SQL file if you need details. Noteworthy are the few non DDL statements at the end of the file, which initialize the database with the built-in properties needed when defining a series in a schema. These properties require in turn the corresponding built-in value types.

These value types are:

name, a string type enforcing a minimalist naming policy

type, for values defining the type of a series

timedomain, a restricted type for time domains, with some predefined values: daily, datetime, monthly, workweek, and yearly

binary, a boolean type

The properties, with their value type in parentheses, are:

Symbol (name)

Type (type)

Calendar (timedomain)

Sparsity (binary)

These value types and properties could in theory be provided virtually by the software, but
in a JDBC implementation they are physically required because of referential integrity.
For more information on time domains, please consult the documentation of the Time2 Library project. For more information on the other properties please consult the documentation of the ChronoDB database project.

At this point the database is ready for useful work directly related to the problem at hand: setting up the schema for the demo. This will be the subject of a forthcoming post.

ChronoDB is a data manager for generic time series written in Java. It supports time series of any type. A flexible schema subsystem allows to organize simply but powerfully very large data sets. The software consists of an API and a generic implementation layer running on top of an SQL or a NoSQL system.

A demo is also available from the same sites. A future post on this blog will explain all about ChronoDB.

Wednesday, March 21, 2012

This maintenance release is plug-compatible with the previous version of the software. The internal management of diagnostic messages and exceptions has been improved in three ways:

Diagnostic messages are fetched and formatted only when actually needed. This improves performance, especially in the case where not all messages are logged by the application environment.

The library has now its own exception type, T2Exception. Because it is a subclass of the exception type used previously there is no compatibility issue.

Messages are now keyed symbolically instead of literally. This provides many benefits to the programmer. One of these is readily visible in the Eclipse IDE where the text of the diagnostic message is displayed as a tooltip when the mouse pointer idles over a message key. The screenshot below shows this in action.

.

As an aside, the snapshot shows a piece of JUnit testing code. Writing software is easier and faster with test-first development. JUnit is a simple and powerful testing pattern for Java. We can thank Beck and Gamma for it. Note that the original idea was developed for Smalltalk by Kent Beck in 1989 and thus predates Java.

Coming soon

A data management system for time series running on top of the Time2 Library is in the pipeline. I hope to release it one of these weeks...